As one of the most ordered clinical tests, hematological analysis has a pivotal role in screening and diagnosing blood conditions and diseases. The evaluation of the blood cell enumeration and morphology through manual microscopic examination is required for pathologists to confirm the peripheral blood smear findings. As a highly informative hematological tool, peripheral blood smear analysis involves highly trained personnel, and expensive equipment, with time-consuming and labor-intensive chemical staining procedures.

In this thesis, a smartphone-based autofluorescence microscopy (Smart-AM) system is demonstrated for imaging blood smears without labels at sub-cellular resolution. The system is created by adding a reversed camera lens module which is well-designed to impede various abe...[ Read more ]

As one of the most ordered clinical tests, hematological analysis has a pivotal role in screening and diagnosing blood conditions and diseases. The evaluation of the blood cell enumeration and morphology through manual microscopic examination is required for pathologists to confirm the peripheral blood smear findings. As a highly informative hematological tool, peripheral blood smear analysis involves highly trained personnel, and expensive equipment, with time-consuming and labor-intensive chemical staining procedures.

In this thesis, a smartphone-based autofluorescence microscopy (Smart-AM) system is demonstrated for imaging blood smears without labels at sub-cellular resolution. The system is created by adding a reversed camera lens module which is well-designed to impede various aberrations and is applicable to work with a cell phone, providing high-quality and label-free blood smear images with a maximum field of view (FOV) of 2.58 mm × 1.94 mm. Apart from the imaging system, this thesis also integrates deep-learning algorithms to Smart-AM (DeepSmart-AM) for automatic detection of different blood cell types and virtual Giemsa staining realization.